import logging
import random
import numpy as np

from collections import defaultdict

from util.util_log import setup_logging
from util.util_csv import save_csv
from util.util_image import save_img, save_img_xyz, save_line_chart
from util.util_ris_pattern import point_2_phi_pattern, phase_2_bit, phase_2_pattern, phase_2_pattern_xyz_fft, \
    phase_2_pattern_xyz, eps

from multi_beam.multi_beam_NN import nn_beam_2_ud, nn_beam_4, nn_beam_8_cub

from util.util_analysis_plane import get_peaks, get_peak_3rd, get_peak_5th, get_peak_9th


# 配置日志，默认打印到控制台，也可以设置打印到文件
setup_logging()
# setup_logging(log_file="../../files/logs/log_multi_beam_PS.log")
# 获取日志记录器并记录日志
logger = logging.getLogger("[RIS-multi-beam-PS-1bit]")


# ============================================= 灰狼优化算法 ===========================================
class RISGreyWolfOptimizer:
    __bit_num = 0  # 比特数
    __beam_num = 0  # 波束数

    def __init__(self, bit_num, beam_num, num_wolves=50, max_iter=100, a_decay=2):
        self.__bit_num = bit_num
        self.__beam_num = beam_num
        self.num_wolves = num_wolves
        self.max_iter = max_iter
        self.a_decay = a_decay  # 攻击参数衰减系数
        self.wolves = None
        self.alpha, self.beta, self.delta = None, None, None
        self.best_fitness = None
        self.best_fitness_history = []
        self.best_individual_history = []

    def __get_psll_2(self, patternBit_mix):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(patternBit_mix) / np.max(np.max(np.abs(patternBit_mix))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第三峰(小于3dB)作为 PSLL
        if len(peaks) > 2:
            peak_3rd = get_peak_3rd(peaks)
            if peak_3rd is not None:
                psll = peak_3rd[0]
        return psll

    def __get_psll_4(self, patternBit_mix):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(patternBit_mix) / np.max(np.max(np.abs(patternBit_mix))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第三峰(小于3dB)作为 PSLL
        if len(peaks) > 4:
            peak_5th = get_peak_5th(peaks)
            if peak_5th is not None:
                psll = peak_5th[0]
        return psll

    def __get_psll_8(self, patternBit_mix):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(patternBit_mix) / np.max(np.max(np.abs(patternBit_mix))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第三峰(小于3dB)作为 PSLL
        if len(peaks) > 8:
            peak_9th = get_peak_9th(peaks)
            if peak_9th is not None:
                psll = peak_9th[0]
        return psll

    def __get_psll(self, patternBit_mix):
        psll = 0
        if self.__beam_num == 2:
            psll = self.__get_psll_2(patternBit_mix)
        elif self.__beam_num == 4:
            psll = self.__get_psll_4(patternBit_mix)
        elif self.__beam_num == 8:
            psll = self.__get_psll_8(patternBit_mix)
        return psll

    def fitness(self, phase_mix):
        # 相位转换 X bit
        phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, self.__bit_num)
        # 计算phase_mix的方向图
        phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
        # patternBit_mix = phase_2_pattern(phaseBit_mix)                # 公式法直接计算, 准确但速度太慢
        patternBit_mix, x, y = phase_2_pattern_xyz_fft(phaseBit_mix)  # FFT法计算, 快速
        # 计算码阵的最大副瓣
        psll = self.__get_psll(patternBit_mix)
        return psll

    # def fitness(self, cluster_init):
    #     list_sum = []
    #     for datas in cluster_init:
    #         sum = 0
    #         for data in datas:
    #             sum += data
    #         list_sum.append(sum)
    #     fit = 0
    #     for sum in list_sum:
    #         fit += sum ** 2
    #     return -fit

    def apply_boundary_conditions(self, wolf):
        # 应用边界条件，确保新位置在合法范围内
        return np.clip(wolf, -180, 180)

    def initialize_pack(self, phase_mix_init):
        # 初始化第一个狼的位置为phase_mix_init
        self.wolves = [np.copy(phase_mix_init)]
        # 对于剩下的狼，基于phase_mix_init进行随机变异
        for _ in range(1, self.num_wolves):
            # 生成一个与phase_mix_init形状相同的布尔掩码，每个位置有10%的概率为True
            mutation_mask = np.random.rand(*phase_mix_init.shape) < 0.20
            # 生成随机偏移量
            random_offsets = np.random.uniform(-180, 180, phase_mix_init.shape)
            # 创建新的狼位置，并应用布尔掩码进行变异
            new_wolf = np.copy(phase_mix_init)
            new_wolf[mutation_mask] += random_offsets[mutation_mask]
            # 应用边界条件
            new_wolf = self.apply_boundary_conditions(new_wolf)
            # 将新位置添加到狼群中
            self.wolves.append(new_wolf)
        # 计算初始适应度并找到α、β、δ
        fitness_scores = [self.fitness(wolf) for wolf in self.wolves]
        sorted_indices = np.argsort(fitness_scores)
        self.alpha = self.wolves[sorted_indices[0]].copy()
        self.beta = self.wolves[sorted_indices[1]].copy()
        self.delta = self.wolves[sorted_indices[2]].copy()
        self.best_fitness = fitness_scores[sorted_indices[0]]
        self.best_fitness_history.append(self.best_fitness)
        self.best_individual_history.append(self.alpha)

    def update_position(self, iteration):
        a = 2 - iteration * ((2) / self.max_iter)  # 更新攻击参数a
        for i, wolf in enumerate(self.wolves):
            r1_alpha = np.random.rand(*wolf.shape)
            r2_alpha = np.random.rand(*wolf.shape)
            A_alpha = 2 * a * r1_alpha - a
            C_alpha = 2 * r2_alpha
            D_alpha = abs(C_alpha * self.alpha - wolf)
            X1 = self.alpha - A_alpha * D_alpha
            #
            r1_beta = np.random.rand(*wolf.shape)
            r2_beta = np.random.rand(*wolf.shape)
            A_beta = 2 * a * r1_beta - a
            C_beta = 2 * r2_beta
            D_beta = abs(C_beta * self.beta - wolf)
            X2 = self.beta - A_beta * D_beta
            #
            r1_delta = np.random.rand(*wolf.shape)
            r2_delta = np.random.rand(*wolf.shape)
            A_delta = 2 * a * r1_delta - a
            C_delta = 2 * r2_delta
            D_delta = abs(C_delta * self.delta - wolf)
            X3 = self.delta - A_delta * D_delta
            #
            new_wolf = (X1 + X2 + X3) / 3  # 更新位置
            # 应用边界条件
            new_wolf = self.apply_boundary_conditions(new_wolf)
            self.wolves[i] = new_wolf

    def run(self, phase_mix_init):
        logger.info("num_wolves=%d, max_iter=%d, a_decay=%d" % (self.num_wolves, self.max_iter, self.a_decay))
        # 初始化返回值
        self.initialize_pack(phase_mix_init)
        # self.best_individual = phase_mix_init
        # self.best_fitness = self.fitness(phase_mix_init)
        # 更新迭代
        for iteration in range(self.max_iter):
            # 更新狼群位置
            self.update_position(iteration)
            # 计算新的适应度值
            fitness_scores = [self.fitness(wolf) for wolf in self.wolves]
            sorted_indices = np.argsort(fitness_scores)
            # 更新α、β、δ
            self.alpha = self.wolves[sorted_indices[0]].copy()
            self.beta = self.wolves[sorted_indices[1]].copy()
            self.delta = self.wolves[sorted_indices[2]].copy()
            # 更新最佳适应度
            if fitness_scores[sorted_indices[0]] < self.best_fitness:
                self.best_fitness = fitness_scores[sorted_indices[0]]
                self.best_individual = self.alpha
            # 记录最佳适应度曲线
            self.best_fitness_history.append(self.best_fitness)
            self.best_individual_history.append(self.best_individual)
            logger.info("iteration=%d: self.best_fitness=%f" % (iteration, self.best_fitness))

        return self.best_individual, self.best_fitness, self.best_fitness_history, self.best_individual_history


# ============================================= 主函数 ====================================
# GWO-NN 核心算法 -- 双波束
def gwo_nn_beam_2(phase1, phase2, bit_num):
    # 1.NN方式初始化结果
    phase_mix = nn_beam_2_ud(phase1, phase2)
    # 2.灰狼优化算法(GWO)寻找最优的phaseBit_mix
    gwo = RISGreyWolfOptimizer(bit_num, 2, 50, 150, 2)
    best_individual, best_fitness, best_fitness_history, best_individual_history = gwo.run(phase_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# GWO-NN 核心算法 -- 四波束
def gwo_nn_beam_4(phase1, phase2, phase3, phase4, bit_num):
    # 1.NN方式初始化结果
    phase_mix = nn_beam_4(phase1, phase2, phase3, phase4)
    # 2.灰狼优化算法(GWO)寻找最优的phaseBit_mix
    gwo = RISGreyWolfOptimizer(bit_num, 4, 50, 150, 2)
    best_individual, best_fitness, best_fitness_history, best_individual_history = gwo.run(phase_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# GWO-NN 核心算法 -- 八波束
def gwo_nn_beam_8(phase1, phase2, phase3, phase4, phase5, phase6, phase7, phase8, bit_num):
    # 1.NN方式初始化结果
    phase_mix = nn_beam_8_cub(phase1, phase2, phase3, phase4, phase5, phase6, phase7, phase8)
    # 2.灰狼优化算法(GWO)寻找最优的phaseBit_mix
    gwo = RISGreyWolfOptimizer(bit_num, 8, 50, 150, 2)
    best_individual, best_fitness, best_fitness_history, best_individual_history = gwo.run(phase_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# 几何分区法 -- 双波束
def main_multi_beam_2(theta1, phi1, theta2, phi2, path_pre, bit_num):
    logger.info("main_multi_beam_2: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("main_multi_beam_2: theta1=%d, phi1=%d, theta2=%d, phi2=%d, " % (theta1, phi1, theta2, phi2))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_2: bit_num bigger than 2.")
        return
    phase1, phaseBit1, pattern1 = point_2_phi_pattern(theta1, phi1, bit_num)
    phase2, phaseBit2, pattern2 = point_2_phi_pattern(theta2, phi2, bit_num)
    # 确保 phase1 和 phase2 具有相同的形状
    assert phaseBit1.shape == phaseBit2.shape, "phase1 和 phase2 必须具有相同的形状"
    # GWO-NN
    phase_mix, best_fitness, best_fitness_history, best_individual_history = gwo_nn_beam_2(phase1, phase2, bit_num)
    # 相位转换 X bit
    phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    #
    # 保存结果
    logger.info("save GWO-NN multi-beam 2 result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存图片
    save_img(path_pre + "phase1.jpg", phase1)
    save_img(path_pre + "phase2.jpg", phase2)
    save_img(path_pre + "phaseBit1.jpg", phaseBit1)
    save_img(path_pre + "phaseBit2.jpg", phaseBit2)
    save_img(path_pre + "pattern1.jpg", pattern1)
    save_img(path_pre + "pattern2.jpg", pattern2)
    save_img(path_pre + "phaseBit_mix.jpg", phaseBit_mix)         # 几何分区法 -- 结果码阵
    save_img(path_pre + "patternBit_mix.jpg", patternBit_mix)     # 几何分区法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phase1, path_pre + "phase1.csv")
    save_csv(phase2, path_pre + "phase2.csv")
    save_csv(phaseBit1, path_pre + "phaseBit1.csv")
    save_csv(phaseBit2, path_pre + "phaseBit2.csv")
    save_csv(phaseBit_mix, path_pre + "phaseBit_mix.csv")
    # 保存灰狼优化算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


# 几何分区法 -- 四波束
def main_multi_beam_4(theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4, path_pre, bit_num):
    logger.info("main_multi_beam_4: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("main_multi_beam_4: theta1=%d, phi1=%d, theta2=%d, phi2=%d, theta3=%d, phi3=%d, theta4=%d, phi4=%d"
                % (theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_N: bit_num bigger than 2.")
        return
    phase1, phaseBit1, pattern1 = point_2_phi_pattern(theta1, phi1, bit_num)
    phase2, phaseBit2, pattern2 = point_2_phi_pattern(theta2, phi2, bit_num)
    phase3, phaseBit3, pattern3 = point_2_phi_pattern(theta3, phi3, bit_num)
    phase4, phaseBit4, pattern4 = point_2_phi_pattern(theta4, phi4, bit_num)
    # 确保所有数组具有相同的形状
    assert phaseBit1.shape == phaseBit2.shape == phaseBit3.shape == phaseBit4.shape, "所有数组必须具有相同的形状"
    # GWO-NN
    phase_mix, best_fitness, best_fitness_history, best_individual_history \
        = gwo_nn_beam_4(phase1, phase2, phase3, phase4, bit_num)
    # 相位转换 X bit
    phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    # 计算phase_mix
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    #
    # 保存结果
    logger.info("save GWO-NN multi-beam 4 result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存图片
    save_img(path_pre + "phase1.jpg", phase1)
    save_img(path_pre + "phase2.jpg", phase2)
    save_img(path_pre + "phase3.jpg", phase3)
    save_img(path_pre + "phase4.jpg", phase4)
    save_img(path_pre + "phaseBit1.jpg", phaseBit1)
    save_img(path_pre + "phaseBit2.jpg", phaseBit2)
    save_img(path_pre + "phaseBit3.jpg", phaseBit3)
    save_img(path_pre + "phaseBit4.jpg", phaseBit4)
    save_img(path_pre + "pattern1.jpg", pattern1)
    save_img(path_pre + "pattern2.jpg", pattern2)
    save_img(path_pre + "pattern3.jpg", pattern3)
    save_img(path_pre + "pattern4.jpg", pattern4)
    save_img(path_pre + "phase_mix.jpg", phaseBit_mix)       # 几何分区法 -- 结果码阵
    save_img(path_pre + "pattern_mix.jpg", patternBit_mix)   # 几何分区法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phase1, path_pre + "phase1.csv")
    save_csv(phase2, path_pre + "phase2.csv")
    save_csv(phase3, path_pre + "phase3.csv")
    save_csv(phase4, path_pre + "phase4.csv")
    save_csv(phaseBit1, path_pre + "phaseBit1.csv")
    save_csv(phaseBit2, path_pre + "phaseBit2.csv")
    save_csv(phaseBit3, path_pre + "phaseBit3.csv")
    save_csv(phaseBit4, path_pre + "phaseBit4.csv")
    save_csv(phaseBit_mix, path_pre + "phase_mix.csv")
    # 保存灰狼优化算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


def main_multi_beam_8(theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4,
                      theta5, phi5, theta6, phi6, theta7, phi7, theta8, phi8,
                      path_pre, bit_num):
    logger.info("main_multi_beam_8: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("main_multi_beam_8: theta1=%d, phi1=%d, theta2=%d, phi2=%d, theta3=%d, phi3=%d, theta4=%d, phi4=%d, "
                "theta5=%d, phi5=%d, theta6=%d, phi6=%d, theta7=%d, phi7=%d, theta8=%d, phi8=%d"
                % (theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4,
                   theta5, phi5, theta6, phi6, theta7, phi7, theta8, phi8))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_8: bit_num bigger than 2.")
        return
    # 获取所有的 phaseBit 变量
    phase1, phaseBit1, pattern1 = point_2_phi_pattern(theta1, phi1, bit_num)
    phase2, phaseBit2, pattern2 = point_2_phi_pattern(theta2, phi2, bit_num)
    phase3, phaseBit3, pattern3 = point_2_phi_pattern(theta3, phi3, bit_num)
    phase4, phaseBit4, pattern4 = point_2_phi_pattern(theta4, phi4, bit_num)
    phase5, phaseBit5, pattern5 = point_2_phi_pattern(theta5, phi5, bit_num)
    phase6, phaseBit6, pattern6 = point_2_phi_pattern(theta6, phi6, bit_num)
    phase7, phaseBit7, pattern7 = point_2_phi_pattern(theta7, phi7, bit_num)
    phase8, phaseBit8, pattern8 = point_2_phi_pattern(theta8, phi8, bit_num)
    # 确保所有数组具有相同的形状
    assert phaseBit1.shape == phaseBit2.shape == phaseBit3.shape == phaseBit4.shape == \
           phaseBit5.shape == phaseBit6.shape == phaseBit7.shape == phaseBit8.shape, "所有数组必须具有相同的形状"
    # GWO-NN
    phase_mix, best_fitness, best_fitness_history, best_individual_history \
        = gwo_nn_beam_8(phase1, phase2, phase3, phase4, phase5, phase6, phase7, phase8, bit_num)
    # 相位转换 X bit
    phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    # 计算phase_mix
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    # 保存结果
    logger.info("save GWO-NN multi-beam 8 result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存图片
    save_img(path_pre + "phase1.jpg", phase1)
    save_img(path_pre + "phase2.jpg", phase2)
    save_img(path_pre + "phase3.jpg", phase3)
    save_img(path_pre + "phase4.jpg", phase4)
    save_img(path_pre + "phase5.jpg", phase5)
    save_img(path_pre + "phase6.jpg", phase6)
    save_img(path_pre + "phase7.jpg", phase7)
    save_img(path_pre + "phase8.jpg", phase8)
    save_img(path_pre + "phaseBit1.jpg", phaseBit1)
    save_img(path_pre + "phaseBit2.jpg", phaseBit2)
    save_img(path_pre + "phaseBit3.jpg", phaseBit3)
    save_img(path_pre + "phaseBit4.jpg", phaseBit4)
    save_img(path_pre + "phaseBit5.jpg", phaseBit5)
    save_img(path_pre + "phaseBit6.jpg", phaseBit6)
    save_img(path_pre + "phaseBit7.jpg", phaseBit7)
    save_img(path_pre + "phaseBit8.jpg", phaseBit8)
    save_img(path_pre + "pattern1.jpg", pattern1)
    save_img(path_pre + "pattern2.jpg", pattern2)
    save_img(path_pre + "pattern3.jpg", pattern3)
    save_img(path_pre + "pattern4.jpg", pattern4)
    save_img(path_pre + "pattern5.jpg", pattern5)
    save_img(path_pre + "pattern6.jpg", pattern6)
    save_img(path_pre + "pattern7.jpg", pattern7)
    save_img(path_pre + "pattern8.jpg", pattern8)
    save_img(path_pre + "phaseBit_mix.jpg", phaseBit_mix)  # 几何分区法 -- 结果码阵
    save_img(path_pre + "patternBit_mix.jpg", patternBit_mix)  # 几何分区法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phase1, path_pre + "phase1.csv")
    save_csv(phase2, path_pre + "phase2.csv")
    save_csv(phase3, path_pre + "phase3.csv")
    save_csv(phase4, path_pre + "phase4.csv")
    save_csv(phase5, path_pre + "phase5.csv")
    save_csv(phase6, path_pre + "phase6.csv")
    save_csv(phase7, path_pre + "phase7.csv")
    save_csv(phase8, path_pre + "phase8.csv")
    save_csv(phaseBit1, path_pre + "phaseBit1.csv")
    save_csv(phaseBit2, path_pre + "phaseBit2.csv")
    save_csv(phaseBit3, path_pre + "phaseBit3.csv")
    save_csv(phaseBit4, path_pre + "phaseBit4.csv")
    save_csv(phaseBit5, path_pre + "phaseBit5.csv")
    save_csv(phaseBit6, path_pre + "phaseBit6.csv")
    save_csv(phaseBit7, path_pre + "phaseBit7.csv")
    save_csv(phaseBit8, path_pre + "phaseBit8.csv")
    save_csv(phaseBit_mix, path_pre + "phaseBit_mix.csv")
    # 保存灰狼优化算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


# ======================================================= main 主方法 ===============================================
def main_multi_gwo_nn():
    # 基于GWO-NN的方法: 主函数
    # 测试方法
    #
    # 几何分区法: 主函数
    main_multi_beam_2(30, 0, 30, 90,
                      "../files/multi-beam/1bit/GWO-NN/2-(30,0,30,90)/", 1)
    main_multi_beam_2(30, 0, 30, 180,
                      "../files/multi-beam/1bit/GWO-NN/2-(30,0,30,180)/", 1)
    main_multi_beam_4(30, 0, 30, 60, 30, 120, 30, 180,
                      "../files/multi-beam/1bit/GWO-NN/4-(30,0,30,60,30,120,30,180)/", 1)
    main_multi_beam_4(30, 0, 30, 90, 30, 180, 30, 270,
                      "../files/multi-beam/1bit/GWO-NN/4-(30,0,30,90,30,180,30,270)/", 1)
    main_multi_beam_8(30, 0, 30, 45, 30, 90, 30, 135, 30, 180, 30, 225, 30, 270, 30, 315,
                      "../files/multi-beam/1bit/GWO-NN/8-(30,45step)/", 1)
    #
    # main_multi_beam_2(30, 0, 30, 90,
    #                   "../files/multi-beam/2bit/GWO-NN/2-(30,0,30,90)/", 2)
    # main_multi_beam_2(30, 0, 30, 180,
    #                   "../files/multi-beam/2bit/GWO-NN/2-(30,0,30,180)/", 2)
    # main_multi_beam_4(30, 0, 30, 60, 30, 120, 30, 180,
    #                   "../files/multi-beam/2bit/GWO-NN/4-(30,0,30,60,30,120,30,180)/", 2)
    # main_multi_beam_4(30, 0, 30, 90, 30, 180, 30, 270,
    #                   "../files/multi-beam/2bit/GWO-NN/4-(30,0,30,90,30,180,30,270)/", 2)
    # main_multi_beam_8(30, 0, 30, 45, 30, 90, 30, 135, 30, 180, 30, 225, 30, 270, 30, 315,
    #                   "../files/multi-beam/2bit/GWO-NN/8-(30,45step)/", 2)





if __name__ == '__main__':
    logger.info("1bit-RIS-multi-beam-PS: GWO based on NN method")
    main_multi_gwo_nn()